Passive tracking of underwater acoustic targets based on multi-beam LOFAR and deep learning
PLoS ONE, ISSN: 1932-6203, Vol: 17, Issue: 12 December, Page: e0273898
2022
- 6Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Conventional passive tracking methods for underwater acoustic targets in sonar engineering generate time azimuth histogram and use it as a basis for target azimuth and tracking. Passive underwater acoustic targets only have azimuth information on the time azimuth histogram, which is easy to be lost and disturbed by ocean noise. To improve the accuracy of passive tracking, we propose to adopt the processed multi-beam Low Frequency Analysis and Recording (LOFAR) as the dataset for passive tracking. In this paper, an improved LeNet-5 convolutional neural network model (CNN) model is used to identify targets, and a passive tracking method for underwater acoustic targets based on multi-beam LOFAR and deep learning is proposed, combined with Extended Kalman Filter (EKF) to improve the tracking accuracy. The performance of the method under realistic conditions is evaluated through simulation analysis and validation using data obtained from marine experiments.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85143182693&origin=inward; http://dx.doi.org/10.1371/journal.pone.0273898; http://www.ncbi.nlm.nih.gov/pubmed/36454946; https://dx.plos.org/10.1371/journal.pone.0273898; https://dx.doi.org/10.1371/journal.pone.0273898; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0273898
Public Library of Science (PLoS)
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